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. 2017 Feb 22:7:43353.
doi: 10.1038/srep43353.

A Comprehensive Analysis of Metabolomics and Transcriptomics in Cervical Cancer

Kai Yang  1 , Bairong Xia  2 , Wenjie Wang  1 , Jinlong Cheng  2 , Mingzhu Yin  3 , Hongyu Xie  1 , Junnan Li  1 , Libing Ma  1 , Chunyan Yang  1 , Ang Li  1 , Xin Fan  4 , Harman S Dhillon  5 , Yan Hou  1   6 , Ge Lou  2 , Kang Li  1
Affiliations

A Comprehensive Analysis of Metabolomics and Transcriptomics in Cervical Cancer

Kai Yang et al. Sci Rep. .

Abstract

Cervical cancer (CC) still remains a common and deadly malignancy among females in developing countries. More accurate and reliable diagnostic methods/biomarkers should be discovered. In this study, we performed a comprehensive analysis of metabolomics (285 samples) and transcriptomics (52 samples) on the potential diagnostic implication and metabolic characteristic description in cervical cancer. Sixty-two metabolites were different between CC and normal controls (NOR), in which 5 metabolites (bilirubin, LysoPC(17:0), n-oleoyl threonine, 12-hydroxydodecanoic acid and tetracosahexaenoic acid) were selected as candidate biomarkers for CC. The AUC value, sensitivity (SE), and specificity (SP) of these 5 biomarkers were 0.99, 0.98 and 0.99, respectively. We further analysed the genes in 7 significantly enriched pathways, of which 117 genes, that were expressed differentially, were mainly involved in catalytic activity. Finally, a fully connected network of metabolites and genes in these pathways was built, which can increase the credibility of our selected metabolites. In conclusion, our biomarkers from metabolomics could set a path for CC diagnosis and screening. Our results also showed that variables of both transcriptomics and metabolomics were associated with CC.

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Conflict of interest statement

The authors declare no competing financial interests.

Figures

Figure 1
Figure 1. PLS-DA three-dimensional score plots and validation plots for the metabolic profiling results.
(a) PLS-DA three-dimensional score plot for CC versus NOR in the ESI+ mode (three latent variables, R2X = 0.211, R2Y = 0.924, Q2 = 0.878). (b) Validation plot for CC versus NOR in ESI+ mode. (c) PLS-DA three-dimensional score plot for CC versus NOR in the ESI- mode (three latent variables, R2X = 0.297, R2Y = 0.917, Q2 = 0.896). (d) Validation plot for CC versus NOR in ESI- mode. The criteria for stability and credibility are as follows: all permuted R2 and Q2 values on the left are lower than the original point on the right, and the Q2 regression line in blue has a negative intercept.
Figure 2
Figure 2. HCA-heatmap plot of 62 differential metabolites between CC and NOR.
Down indicated that these metabolites were down-regulated in cervical cancer patients, Up indicated that these metabolites were up-regulated in cervical cancer patients.
Figure 3
Figure 3. Pie chart of gene functions in 7 pathways.
(a) Pie chart of PANTHER GO-slim molecular function of 117 genes. (b) Pie chart of 91 genes who have the function of catalytic activity (some gene may have more than one function, so the sum of genes is not 91).
Figure 4
Figure 4. Fully connected network of metabolites and genes in our selected 7 pathways.
The nodes in red indicated differential metabolites (1–11) and the nodes in blue indicated differentially expressed genes (12–30) in this study. The nodes in green indicated enzymes in these pathways. 1. 4-Trimethylammoniobutanoic acid. 2. L-Lysine. 3. Palmitic acid. 4. Oleic Acid. 5. Myristic acid. 6. L-Glyceric acid. 7. 21-Deoxycortisol. 8. Oxoglutaric acid. 9. L-Malic acid. 10. L-Histidine. 11. Aldosterone. 12. WHSC1. 13. EHMT2. 14–19. ALDH1B1, ALDH2, ALDH3B1, ALDH3B2, ALDH7A1, ALDH9A1. 20. MAOA. 21–24. CYP1A2, CYP2E1, CYP3A4, CYP19A1. 25. STS. 26–29. SDHA, SDHB, SDHC, SDHD. 30. ACAT1.
Figure 5
Figure 5. An overview workflow of the comprehensive analysis of metabolomics and transcriptomics in cervical cancer.
See this image and copyright information in PMC

References

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